Navigating the Shift: Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systems *

In the world of Cyber-Physical Systems (CPS), a captivating real-time fusion occurs where digital technology meets the physical world. This synergy has been significantly transformed by the integration of artificial intelligence (AI), a move that dramatically enhances system adaptability and introduces a layer of complexity that impacts CPS control optimization and reliability. Despite advancements in AI integration, a significant gap remains in understanding how this shift affects CPS architecture, operational complexity, and verification practices. The extended abstract addresses this gap by investigating architectural distinctions between AI-driven and traditional control models designed in Simulink and their respective implications for system verification.

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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/cain66642.2025.00048
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Navigating the Shift: Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systems *

Khouloud Gaaloul, Hadiza Umar Yusuf
Business Process Modeling and Analysis
article

Navigating the Shift: Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systems *

Khouloud Gaaloul, Hadiza Umar Yusuf
article en

Abstract

In the world of Cyber-Physical Systems (CPS), a captivating real-time fusion occurs where digital technology meets the physical world. This synergy has been significantly transformed by the integration of artificial intelligence (AI), a move that dramatically enhances system adaptability and introduces a layer of complexity that impacts CPS control optimization and reliability. Despite advancements in AI integration, a significant gap remains in understanding how this shift affects CPS architecture, operational complexity, and verification practices. The extended abstract addresses this gap by investigating architectural distinctions between AI-driven and traditional control models designed in Simulink and their respective implications for system verification.

University of Michigan (US)
National Science Foundation
Sustainable cities and communities
Openalex Percentile: Top 77%
Business Process Modeling and Analysis
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